Title: Spatial Downscaling and Cluster Analysis of Flood-Associated Giardiasis Burden in Florida

Abstract: Giardiasis is a waterborne parasitic infection that may be influenced by environmental conditions associated with flooding, storm events, and population exposure. However, public health surveillance data are often reported at broad geographic scales, limiting the ability to identify localized areas where potential burden may be concentrated. This thesis applies a GIS-based spatial downscaling approach to estimate and map flood-associated giardiasis burden across Florida in 2022. Storm-event data from NOAA, FEMA floodplain data, LandScan population estimates, and statewide giardiasis surveillance counts from NNDSS were integrated to construct storm–floodplain risk zones. One-kilometer storm-event buffers were intersected with floodplain areas, exposed population was summarized within each zone, and statewide giardiasis cases were proportionally downscaled using exposed population weights. Global and Local Moran’s I statistics were then used to evaluate spatial clustering in the modeled burden.

The final dataset included 855 storm-floodplain risk zones with a total exposed population of 442,537. Results showed that the downscaled burden was highly concentrated in a small number of zones. Global Moran’s I indicated significant positive spatial autocorrelation, and Local Moran’s I identified High-High clusters primarily in South Florida. These findings show how spatial downscaling and cluster analysis can support public health surveillance planning when disease data are available only at aggregated geographic scales.

Chair: Dr. Alexander Kirpich